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We present a new model for credit index derivatives, in the top-down approach. This model has a dynamic loss intensity process with volatility and jumps and can include counterparty risk. It handles CDS, CDO tranches, Nth-to-default and…

Pricing of Securities · Quantitative Finance 2009-11-10 Louis Paulot

The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing.…

Machine Learning · Statistics 2009-08-27 Guangliang Chen , Gilad Lerman

This article presents a generic framework for modeling the dynamics of forward curves in commodity market as commodity derivatives are typically traded by futures or forwards. We have theoretically demonstrated that commodity prices are…

Pricing of Securities · Quantitative Finance 2026-02-26 David Xiao

We study the affine schemes of modules over gentle algebras. We describe the smooth points of these schemes, and we also analyze their irreducible components in detail. Several of our results generalize formerly known results, e.g. by…

Representation Theory · Mathematics 2021-12-23 Christof Geiß , Daniel Labardini-Fragoso , Jan Schröer

Multiphase flows are an important class of fluid flow and their study facilitates the development of diverse applications in industrial, natural, and biomedical systems. We consider a model that uses a continuum description of both phases…

Fluid Dynamics · Physics 2025-08-04 Bindi M. Nagda , Aaron Barrett , Boyce E. Griffith , Aaron L. Fogelson , Jian Du

It is a market practice to express market-implied volatilities in some parametric form. The most popular parametrizations are based on or inspired by an underlying stochastic model, like the Heston model (SVI method) or the SABR model (SABR…

Mathematical Finance · Quantitative Finance 2026-01-06 Nicola F. Zaugg , Leonardo Perotti , Lech A. Grzelak

We propose and experimentally demonstrate an innovative stock index prediction method using a weighted optical reservoir computing system. We construct fundamental market data combined with macroeconomic data and technical indicators to…

Machine Learning · Computer Science 2024-08-02 Fang Wang , Ting Bu , Yuping Huang

Multi-calibration is a powerful and evolving concept originating in the field of algorithmic fairness. For a predictor $f$ that estimates the outcome $y$ given covariates $x$, and for a function class $\mathcal{C}$, multi-calibration…

Machine Learning · Computer Science 2023-03-09 Zhun Deng , Cynthia Dwork , Linjun Zhang

A new modelling approach that directly prescribes dynamics to the term structure of VIX futures is proposed in this paper. The approach is motivated by the tractability enjoyed by models that directly prescribe dynamics to the VIX,…

Mathematical Finance · Quantitative Finance 2015-04-03 Alexander Badran , Beniamin Goldys

We consider a Black-Scholes market in which a number of stocks and an index are traded. The simplified Capital Asset Pricing Model is the conjunction of the usual Capital Asset Pricing Model, or CAPM, and the statement that the appreciation…

Portfolio Management · Quantitative Finance 2011-11-14 Vladimir Vovk

Given the promising results on joint modeling of SPX/VIX smiles of the recently introduced quadratic rough Heston model, we consider a multi-asset market making problem on SPX and its derivatives, e.g. VIX futures, SPX and VIX options. The…

Mathematical Finance · Quantitative Finance 2022-12-21 Mathieu Rosenbaum , Jianfei Zhang

We apply the concepts of utility based pricing and hedging of derivatives in stochastic volatility markets and introduce a new class of "reciprocal affine" models for which the indifference price and optimal hedge portfolio for pure…

Probability · Mathematics 2008-12-02 M. R. Grasselli , T. R. Hurd

We study a functional linear regression model that deals with functional responses and allows for both functional covariates and high-dimensional vector covariates. The proposed model is flexible and nests several functional regression…

Statistics Theory · Mathematics 2022-08-24 Daren Wang , Zifeng Zhao , Yi Yu , Rebecca Willett

We study generalised additive models, with shape restrictions (e.g. monotonicity, convexity, concavity) imposed on each component of the additive prediction function. We show that this framework facilitates a nonparametric estimator of each…

Statistics Theory · Mathematics 2014-04-14 Yining Chen , Richard J. Samworth

We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have…

General Finance · Quantitative Finance 2012-10-23 Ulrich Horst , Michael Kupper , Andrea Macrina , Christoph Mainberger

In fixed income sector, the yield curve is probably the most observed indicator by the market for trading and fifinancing purposes. A yield curve plots interest rates across different contract maturities from short end to as long as 30…

Mathematical Finance · Quantitative Finance 2018-08-13 Jian Sun

Market makers provide liquidity to other market participants: they propose prices at which they stand ready to buy and sell a wide variety of assets. They face a complex optimization problem with both static and dynamic components. They…

Trading and Market Microstructure · Quantitative Finance 2017-05-09 Olivier Guéant

Inside the discipline of graph theory exists an extension known as the hypergraph. This generalization of graphs includes vertices along with hyperedges consisting of collections of two or more vertices. One well-studied application of this…

Probability · Mathematics 2024-03-19 Joshua Sparks

We present a topological framework for finding low-flop algorithms for evaluating element stiffness matrices associated with multilinear forms for finite element methods posed over straight-sided affine domains. This framework relies on…

Numerical Analysis · Mathematics 2012-05-15 Robert C. Kirby , Anders Logg , L. Ridgway Scott , Andy R. Terrel

Recently, a unified framework for adaptive kernel based signal processing of complex data was presented by the authors, which, besides offering techniques to map the input data to complex Reproducing Kernel Hilbert Spaces, developed a…

Machine Learning · Computer Science 2015-05-30 Pantelis Bouboulis , Sergios Theodoridis , Michael Mavroforakis